ICE techniques are variations of multivariate optical elements (“MOE”) which were originally targeted for qualitative and quantitative analysis of physical or chemical properties of interest in chemometrics. In recent years, ICE techniques have been developed for applications in the Oil and Gas Industry in the form of optical sensors on downhole or surface equipment to evaluate a variety of fluid properties. ICE structures typically consist of multiple physical layers with different refractive indexes in the film material, wherein their optical or spectroscopic characteristics, if designed properly, can be transformed into effective inputs for linear and nonlinear multivariate calibration.
ICE design optimization is crucial in providing appropriate candidates for performance and fabricability analysis and manufacturing. Conventional ICE design begins with a very large number of structure simulations such as, 100,000 or more, for example. In each original design, first the number of ICE layers and layer thickness are randomly initiated. Next, a gradient-based optimization routine is performed by calculating the first derivative or the second derivative (i.e., Gauss-Newton Method) to update the layer thickness. Then, layers are deleted or combined to implement the preliminary ICE structure. The candidate solutions, with significantly reduced numbers of designs, are then selected through a post-processing procedure that applies multiple thresholds, such as the maximum and minimum number of layers, minimum single film thickness, maximum total film thickness and minimum system percentage transmittance.
There are a number of disadvantages associated with conventional ICE design optimization approaches. First, conventional approaches begin with a voluminous number of preliminary designs in order to maximize the likelihood that the most feasible candidates are analyzed, which may take 10-12 hours, in general, to perform the simulations. Second, due to the volume of preliminary designs and the candidate finalization process, which involves spectrum visualization, parameter cross-plotting and tolerance-based fabrication analysis, existing approaches require an enormous amount of computing power. As a result, high intensity simulation and dramatic post processing are necessary which require design suite systems having 15-node computer clusters (master and nodes) or multiple processors that present extra hardware and software requirements. Third, conventional methodologies utilize a single-objective (standard error of calibration, for example) function to determine calibration coefficients for the ICE structures, which often conflicts with other important measures that may be crucial for fabricability of the designs.
In view of the foregoing, there is a need in the art for an efficient, cost-effective ICE design optimization technique which reduces computing requirements and computation cost.